CN112312444A - Resource backup method under 5G network slice - Google Patents
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Abstract
The invention provides a resource backup method under a 5G network slice, which comprises the steps of calculating the resource quantity of each network node according to the calculation resource quantity of each network node, the storage resource quantity of each network node and the bandwidth resource quantity of a link of each network node; selecting network nodes with larger resource quantity in a preset quantity proportion from each cluster according to the resource quantity of each network node, and placing the network nodes into an alternative resource pool; calculating the historical fault evaluation value of each network node in the alternative resource pool according to the self fault occurrence frequency of each network node in the preset time, the link fault occurrence frequency between each network node and other nodes in the preset time and the link fault occurrence frequency between other nodes and each network node in the preset time; and selecting a preset number of network nodes with the maximum historical fault evaluation values in the alternative resource pool for backup. By the invention, the problem that the existing 5G network simultaneously breaks down in a large scale is solved.
Description
Technical Field
The invention relates to the technical field of 5G communication, in particular to a resource backup method under a 5G network slice.
Background
With the rapid development and application of 5G network technology, a virtualized Evolved Packet Core (vEPC) based on a slicing technology has gradually become one of the Core network main technologies of the 5G network. Under the vEPC technology architecture, a network virtualization technology is taken as a key technology of the network architecture, and original network facilities are divided into a basic network and a virtual network. The basic network provides basic network resources for the virtual network, and the virtual network is used for bearing various 5G services. There have been many studies on how the infrastructure network allocates resources to the virtual network. The main research focuses on improving the resource utilization. In order to ensure reliability and stability of network traffic, research to improve network reliability has been increasing in recent years.
The existing research can be divided into two types of improving the reliability of the network and improving the fault recovery speed. In the aspect of improving Network reliability research, documents [ Hawilo H, Shami A, Mirahmadi M, et al.NFV: state of the art, classes, and replication in next generation mobile networks (vEPC) [ J ]. IEEE networks, 2014,28(6):18-26 ] aim at the problem that a single Network link has a fault and adopt a strategy of rapid migration inside a Network node to realize rapid recovery of fault resources. For the problem of Network node and Network link failure in a distributed environment, the document [ Mijumbi R, Serrat J, Gorricho J L, et al.design and evaluation of algorithms for mapping and scheduling of virtual Network functions [ C ]// Proceedings of the 20151st IEEE Conference on Network protocol (NetSoft). IEEE,2015:1-9 ] proposes a flexible self-adaptive virtual Network mapping algorithm, and improves the survivability of the Network. The document [ Yousaf F Z, Loureiro P, ZDarsky F, et al. cost analysis of initial deployment strategies for virtual organized core networks [ J ]. IEEE Communications major, 2015,53(12):60-66 ] proposes a resource backup location selection algorithm for network node failure problems in a single area on the premise of network failure sequence occurrence. The document [ Saraswat R, Narayanamurthy G, Maheshwari A K. traffic capacity based optimization of SOA fault recovery using linear mapping model: U.S. patent 9,430,319[ P ].2016-8-30 ] aims at the problem of failure recovery is not considered in the remapping algorithm, the failure recovery problem of virtual network mapping is modeled based on the linear programming algorithm, and a self-adaptive automatic remapping algorithm is provided, so that the network failure recovery speed is improved.
As can be seen from the analysis of the existing research, the existing research mainly takes the small-range and local faults as the main research content. However, with the rapid increase in the network size, a case where a plurality of failures occur simultaneously occurs rapidly. How to solve the problem of network reliability when multiple network faults occur simultaneously in a large-scale network environment becomes a research problem which needs to be solved urgently.
Disclosure of Invention
The technical problem to be solved by the present invention is to provide a resource backup method under a 5G network slice, which is used for solving the problem of network reliability when a plurality of existing networks fail.
The invention provides a resource backup method under a 5G network slice, which comprises the following steps:
step S101, calculating the resource amount of each network node according to the calculation resource amount of each network node, the storage resource amount of each network node, the bandwidth resource amount of each link of each network node, and a first preset coefficient, a second preset coefficient and a third preset coefficient which respectively correspond to the calculation resource amount, the storage resource amount and the bandwidth resource amount of each link;
step S102, selecting network nodes with larger resource quantity in a preset quantity proportion from each cluster and placing the network nodes into an alternative resource pool according to the resource quantity of each network node;
step S103, calculating a historical fault evaluation value of each network node in the alternative resource pool according to the self fault occurrence frequency of each network node in preset time, the link fault occurrence frequency of each network node with other nodes in the preset time and the link fault occurrence frequency of other nodes with each network node in the preset time;
step S104, selecting a preset number of network nodes with the maximum historical fault evaluation value in the alternative resource pool to perform the backup of the storage resource quantity and the calculation resource quantity, and performing bandwidth resource backup on links of the preset number of nodes of the network with the maximum historical fault evaluation value.
Further, before the step S101, the method further includes:
step S201, dividing a 5G network containing a first number of network nodes into a second number of clusters;
step S202, randomly selecting the second number of network nodes from the first number of network nodes as the clustering centers of the clusters, wherein the first number is greater than or equal to the second number;
step S203, calculating a first Euclidean distance between the network nodes in the clusters and the cluster centers of the clusters according to the cluster center of each cluster and the network nodes in the clusters, and calculating a second Euclidean distance between the cluster centers of any two clusters according to the cluster centers of any two clusters;
step S204, calculating the membership degree of each network node according to the first Euclidean distance, the second Euclidean distance and a preset weight factor;
step S205, calculating a first clustering benefit of each network node according to the first Euclidean distance and the membership degree of each network node;
step S206, judging whether the iteration times of the preset algorithm meet the iteration time limit, and whether the initial temperature value and the termination temperature value meet the temperature limit;
step S207, if the first quantity of network nodes is not the second quantity of clusters, solving a new clustering center for the second quantity of clusters according to the first quantity of network nodes, the membership degree of each network node and the preset weight factor;
step S208, repeating the steps S202 to S204 according to the new clustering center, and solving the second clustering benefit of each network node;
step S209, when the second clustering gain of the network node is greater than the first clustering gain of the network node, replacing the original clustering center with the new clustering center;
step S210, when the second clustering profit of the network node is less than or equal to the first clustering profit of the network node, calculating the probability of replacing the original clustering center by the new clustering center according to the first clustering profit, the second clustering profit and the current temperature.
Further, the formula of step S204 is specifically:
the muikFor each degree of membership of a network node, said dikIs a first Euclidean distance, which represents cluster AkInner network node niAnd the cluster AkA first Euclidean distance of the cluster centers of (a), said i ranges from 1 to n, said n is said first number, said k ranges from 1 to c, said c is said second number; d isjkIs the second euclidean distance and,it represents a cluster AkAnd cluster AjA second Euclidean distance between cluster centers of (a), said j ranges from 1 to c, said c being said second number; and b is a preset weight factor.
Further, the formula for implementing step S205 is:
Further, the formula for implementing step S207 is:
Further, the formula for implementing step S101 is specifically:
Further, the formula for implementing step S103 is specifically:
Further, the method further comprises:
for the network node for backup, expanding the link of the network node for backup.
Further, the step S104 specifically includes:
and selecting the three network nodes with the maximum historical fault evaluation value in the alternative resource pool to perform storage resource quantity and calculation resource quantity backup, wherein the storage resource quantity of each network node backup is one third of the storage resource quantity of the network node with the maximum historical fault evaluation value, and the calculation resource quantity of each network node backup is one third of the calculation resource quantity of the network node with the maximum historical fault evaluation value.
Further, for each network node used for backup, the expanded bandwidth amount is one third of the bandwidth amount of the network node with the largest historical failure evaluation value.
The implementation of the invention has the following beneficial effects:
according to the invention, the 5G network is clustered, the network node with the largest resource amount is selected in the cluster and put into the alternative resource pool, the historical fault evaluation value is calculated for the network node which is selected into the alternative resource pool, and the storage resource amount, the calculation resource amount and the bandwidth amount of the link are backed up for the preset number of network nodes with the largest historical fault evaluation value; the problem of network reliability when a plurality of network failures of the existing large-scale 5G network occur simultaneously is solved.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a block diagram of a 5G network provided in the prior art.
Fig. 2 is a flowchart of a resource backup method under a 5G network slice according to an embodiment of the present invention.
Fig. 3 is a flowchart of a clustering and optimizing method for a 5G network according to an embodiment of the present invention.
Fig. 4 is a schematic diagram illustrating an influence of a network size on a resource backup method under a 5G network slice according to an embodiment of the present invention.
Fig. 5 is a schematic diagram illustrating an influence of an underlying network failure rate on a resource backup method under a 5G network slice according to an embodiment of the present invention.
Detailed Description
In this patent, the following description will be given with reference to the accompanying drawings and examples.
As shown in fig. 1, the 5G core network architecture includes multiple vepcs and network links. Each vpec (Virtual Evolved Packet Core) includes four kinds of main devices, namely, an MME (Mobility Management Entity), an HSS (Home Subscriber Server), an SGW (Serving Gateway), and a PGW (Packet data network Gateway). After a network function virtualization technology is introduced, MME, HSS, SGW and PGW equipment in each vEPC run on a unified physical server, and resource allocation and management are realized by using the virtualization technology. After using network virtualization technology, the 5G core network is divided into a virtual network and an underlying network. The underlying network provides network node and network link resources for the virtual network. The virtual network carries various 5G services by leasing basic network resources.
As can be seen from the description of the network architecture of the 5G core network, the core network is connected by a plurality of vepcs via network links. Therefore, the present invention represents the underlying network as a undirected weighted graph GS＝(NS,ES) Wherein N isSRepresenting a set of underlying network nodes. As can be seen from fig. 1, each base network node is a vsepc, and includes four types of sub-resources, namely MME, HSS, SGW, and PGW. ESRepresenting the set of links of the underlying network. For the bottom node
Including node storage resources
Node computing resource
Node location
Three attributes, the invention uses
Representing a collection of attributes. For underlying network links
Including bandwidth attributes, usage
Representing the bandwidth resources of the network link.
The present invention represents a virtual network as a undirected weighted graph GV＝(NV,EV). Wherein N isVRepresenting a collection of virtual network nodes. EVRepresenting a set of virtual links. For virtual network nodes
It includes node storage resources
Node computing resource
Node location
Three types of attributes, use
And (4) showing. For each virtual link
Including bandwidth attribute usage
And (4) showing.
For each virtual network, the time for renting the underlying network is limited, and the renting time is called the life cycle of the virtual network and is used
And (4) showing. In order to allocate the resources of the underlying network to the virtual network, the invention allocates the resources of the underlying network to the virtual network, which is called as the virtual network mapping process, and uses MN:(NV→NS,EV→PS) And (4) showing. Wherein, PSAn underlying path representing a virtual link map includes a plurality of connected underlying links.
As shown in fig. 2, an embodiment of the present invention provides a method for backing up resources under a 5G network slice, where the method includes:
step S101, calculating the resource amount of each network node according to the calculation resource amount of each network node, the storage resource amount of each network node, the bandwidth resource amount of each link of each network node, and a first preset coefficient, a second preset coefficient and a third preset coefficient respectively corresponding to the calculation resource amount, the storage resource amount and the bandwidth resource amount of each link.
For step S101, the calculation formula is:
wherein R isvEPCFor the resource amount of each network node, α, β, γ are the first preset coefficient, the second preset coefficient and the third preset coefficient respectively, and the
Representing network nodes
Home network node set NSSaid
Representing links of network nodes
Link set E of home network nodesSSaid
For each network node, the amount of computing resources
For each network node, the amount of storage resources
The amount of bandwidth resources for the link for each network node.
And S102, selecting network nodes with larger resource quantity in a preset quantity proportion from each cluster and placing the network nodes into an alternative resource pool according to the resource quantity of each network node.
Step S103, calculating the historical fault evaluation value of each network node in the alternative resource pool according to the self fault occurrence frequency of each network node in the preset time, the link fault occurrence frequency of each network node with other nodes in the preset time and the link fault occurrence frequency of other nodes with each network node in the preset time.
It should be noted that the number of times of the self failure of each network node within the preset time, the number of times of the link failure of each network node with other nodes within the preset time, and the number of times of the link failure of other nodes with each network node within the preset time are obtained by the network manager, and the other nodes refer to all network nodes connected with each network node.
The concrete implementation formula of step S103 is:
It should be noted that, in theory, the
Is the same link, but the values of the two may be different in consideration of network noise, so the summation of the two is adopted here.
Step S104, selecting a preset number of network nodes with the maximum historical fault evaluation value in the alternative resource pool to perform the backup of the storage resource quantity and the calculation resource quantity, and performing bandwidth resource backup on links of the preset number of nodes of the network with the maximum historical fault evaluation value.
Further, the method further comprises:
and for each network node for backup, expanding the link of the network node for backup, wherein the expanded bandwidth amount is one third of the bandwidth amount of the network node with the maximum historical fault evaluation value.
The implementation step S104 specifically includes: and selecting three network nodes with larger historical fault evaluation values in the alternative resource pool to perform storage resource quantity and calculation resource quantity backup, wherein the storage resource quantity of each network node backup is one third of the storage resource quantity of the network node with the largest historical fault evaluation value, and the calculation resource quantity of each network node backup is one third of the calculation resource quantity of the network node with the larger historical fault evaluation value.
It is contemplated that resource recovery may be achieved by fast migration of resources within individual vepcs. Therefore, the experimental part mainly verifies the performance of the algorithm of the invention when a certain vEPC fails in its entirety. In the experimental part, the present invention uses GT-ITM tool [ ZEGURA E W, CAVERT K L, BHATTACHARJEE S.How to model an Internet work [ C ]// IEEE Infocom, C1996: 594-. The size of the network nodes of the underlying network is increased from 100 to 500. The number of network nodes of the virtual network obeys a uniform distribution of [3,10 ]. In terms of network links, the probability of any two nodes of the base network and the virtual network being connected is 0.3, in terms of storage resources of the network nodes, computing resources of the network nodes and bandwidth resources of the network links, the base network is subject to uniform distribution of [25,50], and the virtual network is subject to uniform distribution of [1,5 ].
In terms of performance comparison, the inventive algorithm RRBFRM is compared with a Randomly Selected Backup Mechanism (RSBM), wherein the algorithm RSBM and the inventive algorithm use the same amount of resources as backup resources. The comparison index is network reliability, using the following formula
A calculation is performed in which, among other things,
the number of nodes which are successfully recovered after the nodes are failed is shown, and X shows the number of the nodes which are failed together.
To verify the recovery capability of the algorithm, resources are first allocated using the basic mapping algorithm [ Fischer A, Botero J F, Beck M T, et al. virtual Network Embedding: A surfey [ J ]. IEEE Communications Surveys & Tutorials,2013,15(4):1888 + 1906 ], until the mapping success rate is below 40%. Then the algorithm RRBFRM and the comparison algorithm RSBM of the invention are used for backup. When fault simulation is carried out, the faults of all network nodes change from 1% to 10%, and performance comparison is carried out after the algorithm and the comparison algorithm are recovered.
In the aspect of performance analysis, the influence on the reliability of the algorithm is analyzed from the aspects of network scale and failure rate of the underlying network.
The experimental results are shown in fig. 4 in terms of the effect of network size on the reliability of the algorithm. The X-axis represents the increase in the number of underlying network nodes from 100 to 500, and the Y-axis represents network reliability. As can be seen from fig. 4, as the network size increases, the recovery capability of both algorithms to the failure is relatively stable. The network size has small influence on the recovery capability of the algorithm. The performance analysis of the two algorithms shows that the recovery capability of the algorithm is higher than that of the traditional algorithm, and the resource backup mechanism of the algorithm has better advantages.
The experimental results are shown in fig. 5 in terms of the influence of the failure rate of the underlying network on the reliability of the algorithm. The X-axis represents the increase in the underlying network failure rate from 1% to 10%, and the Y-axis represents the network reliability. It can be seen from the figure that as the failure rate of the underlying network increases, the recovery capability of both algorithms to the failure decreases rapidly. The failure rate of the underlying network is increased, more resources are needed for failure recovery, but the capacity of resource backup is low, and the requirement of large amount of resource recovery cannot be met. The performance analysis of the two algorithms shows that the recovery capability of the algorithm is higher than that of the traditional algorithm, which shows that the resource backup mechanism of the algorithm can recover more fault resources, thereby improving the network reliability.
As shown in fig. 3, an embodiment of the present invention provides a 5G network clustering and optimizing method, where the method includes:
step S201, a 5G network comprising a first number of network nodes is divided into a second number of clusters.
In this embodiment, the fuzzy C-means clustering based on the genetic algorithm and the simulated annealing algorithm has a performance advantage of good classification effect, and the 5G network including n network nodes is divided into C clusters by using the above algorithm, where the value range of C is (C is greater than or equal to 2 and less than or equal to n), that is, each network node can belong to one cluster, and also a plurality of network nodes can belong to one cluster.
Step S202, randomly selecting the second number of network nodes from the first number of network nodes as the clustering centers of the clusters, wherein the first number is larger than or equal to the second number.
Step S203, calculating a first Euclidean distance between the network nodes in the clusters and the cluster centers of the clusters according to the cluster centers of each cluster and the network nodes in the clusters, and calculating a second Euclidean distance between the cluster centers of any two clusters according to the cluster centers of any two clusters.
And S204, calculating the membership degree of each network node according to the first Euclidean distance, the second Euclidean distance and a preset weight factor.
The formula of step S204 is specifically:
the muikFor each degree of membership of a network node, said dikIs a first Euclidean distance, which represents cluster AkInner network node niAnd the cluster AkA first Euclidean distance of the cluster centers of (a), said i ranges from 1 to n, said n is said first number, said k ranges from 1 to c, said c is said second number; d isjkIs a second Euclidean distance, which represents cluster AkAnd cluster AjA second Euclidean distance between cluster centers of (a), said j ranges from 1 to c, said c being said second number; and b is a preset weight factor.
And S205, calculating the first clustering benefit of each network node according to the first Euclidean distance and the membership degree of each network node.
The formula for implementing step S205 is:
Step S206, judging whether the iteration times of the preset algorithm meet the iteration time limit, and whether the initial temperature value and the termination temperature value meet the temperature limit.
And step S207, if not, solving a new clustering center for the second number of clusters according to the first number of network nodes, the membership degree of each network node and the preset weight factor.
The formula for implementing step S207 is:
v isiIs newI is the number of clusters; the muikMembership for each network node; n iskFor each network node, b is a preset weighting factor.
And S208, repeating the steps S202 to S204 according to the new clustering center, and solving the second clustering benefit of each network node.
Step S209, when the second clustering gain of the network node is greater than the first clustering gain of the network node, replacing the original clustering center with the new clustering center.
Step S210, when the second clustering profit of the network node is less than or equal to the first clustering profit of the network node, calculating the probability of replacing the original clustering center by the new clustering center according to the first clustering profit, the second clustering profit and the current temperature.
It should be noted that steps S201 to S210 may be performed before step S102, and the purpose of steps S201 to S210 is to cluster the 5G network and optimize the clustering, so as to provide a good clustering basis for subsequently selecting the candidate resource pool, calculating the historical failure evaluation value, and performing resource backup.
The implementation of the invention has the following beneficial effects:
according to the invention, the 5G network is clustered, the network node with the largest resource amount is selected in the cluster and put into the alternative resource pool, the historical fault evaluation value is calculated for the network node which is selected into the alternative resource pool, and the storage resource amount, the calculation resource amount and the bandwidth amount of the link are backed up for the preset number of network nodes with the largest historical fault evaluation value; the problem of network reliability when a plurality of network failures of the existing large-scale 5G network occur simultaneously is solved.
The foregoing is a more detailed description of the invention in connection with specific preferred embodiments and it is not intended that the invention be limited to these specific details. For those skilled in the art to which the invention pertains, several simple deductions or substitutions can be made without departing from the spirit of the invention, and all shall be considered as belonging to the protection scope of the invention.
Claims (10)
1. A method for backing up resources under a 5G network slice is characterized by comprising the following steps:
step S101, calculating the resource amount of each network node according to the calculation resource amount of each network node, the storage resource amount of each network node, the bandwidth resource amount of each link of each network node, and a first preset coefficient, a second preset coefficient and a third preset coefficient which respectively correspond to the calculation resource amount, the storage resource amount and the bandwidth resource amount of each link;
step S102, selecting network nodes with larger resource quantity in a preset quantity proportion from each cluster and placing the network nodes into an alternative resource pool according to the resource quantity of each network node;
step S103, calculating a historical fault evaluation value of each network node in the alternative resource pool according to the self fault occurrence frequency of each network node in preset time, the link fault occurrence frequency of each network node with other nodes in the preset time and the link fault occurrence frequency of other nodes with each network node in the preset time;
step S104, selecting a preset number of network nodes with the maximum historical fault evaluation value in the alternative resource pool to perform the backup of the storage resource quantity and the calculation resource quantity, and performing bandwidth resource backup on links of the preset number of nodes of the network with the maximum historical fault evaluation value.
2. The method of claim 1, wherein the step S101 further comprises:
step S201, dividing a 5G network containing a first number of network nodes into a second number of clusters;
step S202, randomly selecting the second number of network nodes from the first number of network nodes as the clustering centers of the clusters, wherein the first number is greater than or equal to the second number;
step S203, calculating a first Euclidean distance between the network nodes in the clusters and the cluster centers of the clusters according to the cluster center of each cluster and the network nodes in the clusters, and calculating a second Euclidean distance between the cluster centers of any two clusters according to the cluster centers of any two clusters;
step S204, calculating the membership degree of each network node according to the first Euclidean distance, the second Euclidean distance and a preset weight factor;
step S205, calculating a first clustering benefit of each network node according to the first Euclidean distance and the membership degree of each network node;
step S206, judging whether the iteration times of the preset algorithm meet the iteration time limit, and whether the initial temperature value and the termination temperature value meet the temperature limit;
step S207, if the first quantity of network nodes is not the second quantity of clusters, solving a new clustering center for the second quantity of clusters according to the first quantity of network nodes, the membership degree of each network node and the preset weight factor;
step S208, repeating the steps S202 to S204 according to the new clustering center, and solving the second clustering benefit of each network node;
step S209, when the second clustering gain of the network node is greater than the first clustering gain of the network node, replacing the original clustering center with the new clustering center;
step S210, when the second clustering profit of the network node is less than or equal to the first clustering profit of the network node, calculating the probability of replacing the original clustering center by the new clustering center according to the first clustering profit, the second clustering profit and the current temperature.
3. The method according to claim 2, wherein the formula of step S204 is specifically:
the muikFor each degree of membership of a network node, said dikIs a first Euclidean distance, which represents cluster AkInner network node niAnd the cluster AkA first Euclidean distance of the cluster centers of (a), said i ranges from 1 to n, said n is said first number, said k ranges from 1 to c, said c is said second number; d isjkIs a second Euclidean distance, which represents cluster AkAnd cluster AjA second Euclidean distance between cluster centers of (a), said j ranges from 1 to c, said c being said second number; and b is a preset weight factor.
4. The method of claim 3, wherein the formula for implementing step S205 is:
5. The method of claim 1, wherein the formula for implementing step S207 is:
6. The method of claim 1, wherein the step S101 is performed according to the following formula:
7. The method of claim 1, wherein the formula for implementing step S103 is specifically:
wherein said
Is the historical failure evaluation value of the ith network node, the aiiIs the number of times of the failure of the ith network node in a preset time period k, wherein aijIs that the link between the ith network node and the jth network node is in the preset time periodNumber of failures in k, said ajiIs the number of times that the link between the jth network node and the ith network node fails within a preset time period k.
8. The method of claim 1, wherein the method further comprises:
for the network node for backup, expanding the link of the network node for backup.
9. The method according to claim 8, wherein the step S104 specifically includes:
and selecting the three network nodes with the maximum historical fault evaluation value in the alternative resource pool to perform storage resource quantity and calculation resource quantity backup, wherein the storage resource quantity of each network node backup is one third of the storage resource quantity of the network node with the maximum historical fault evaluation value, and the calculation resource quantity of each network node backup is one third of the calculation resource quantity of the network node with the maximum historical fault evaluation value.
10. The method of claim 9, wherein for each network node used for backup, the amount of bandwidth expanded is one-third of the amount of bandwidth of the network node having the largest historical failure rating value.
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| CN113038520A (en) * | 2021-03-31 | 2021-06-25 | 广东电网有限责任公司电力调度控制中心 | Topology-aware virtual network service fault recovery method and system |
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| CN113114514A (en) * | 2021-05-07 | 2021-07-13 | 广东电网有限责任公司电力调度控制中心 | Network resource backup method and system based on multi-attribute analytic hierarchy process |
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| CN113038520A (en) * | 2021-03-31 | 2021-06-25 | 广东电网有限责任公司电力调度控制中心 | Topology-aware virtual network service fault recovery method and system |
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